{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-based-optimization-of-the-under","title":"Learning-based Optimization of the Under-sampling Pattern in MRI","arxiv_id":"1901.01960","date":"2019-01-07","proceeding":null,"authors":["Cagla Deniz Bahadir","Adrian V. Dalca","Mert R. Sabuncu"],"abstract":"Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by\nunder-sampling in k-space (i.e., the Fourier domain). In this paper, we\nconsider the problem of optimizing the sub-sampling pattern in a data-driven\nfashion. Since the reconstruction model's performance depends on the\nsub-sampling pattern, we combine the two problems. For a given sparsity\nconstraint, our method optimizes the sub-sampling pattern and reconstruction\nmodel, using an end-to-end learning strategy. Our algorithm learns from\nfull-resolution data that are under-sampled retrospectively, yielding a\nsub-sampling pattern and reconstruction model that are customized to the type\nof images represented in the training data. The proposed method, which we call\nLOUPE (Learning-based Optimization of the Under-sampling PattErn), was\nimplemented by modifying a U-Net, a widely-used convolutional neural network\narchitecture, that we append with the forward model that encodes the\nunder-sampling process. Our experiments with T1-weighted structural brain MRI\nscans show that the optimized sub-sampling pattern can yield significantly more\naccurate reconstructions compared to standard random uniform, variable density\nor equispaced under-sampling schemes. The code is made available at:\nhttps://github.com/cagladbahadir/LOUPE .","url_abs":"http://arxiv.org/abs/1901.01960v2","url_pdf":"http://arxiv.org/pdf/1901.01960v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-based-optimization-of-the-under","repo_url":"https://github.com/cagladbahadir/LOUPE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.01960","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}